Hyper-targetedCustomer Retention

with

Customer 360

But to truly realize the benefits of customer retention programs, marketers need to design and deliver communications that posit ively influence the behavior of each individual customer so as to maximize the benefit for the retai ler as well as the customer.

Before a retai ler can make any progress in retaining existing customers, he first needs to recognize the true cost of customer attrit ion. Next he needs to understand purchase behavior and preferences of al l of his customers and then accurately identify the customers who are displaying behavior that indicates a high risk for churn. Finally he needs to engage with these customers using a focused and personalized approach to retain their loyalty and thus drive revenue growth.

Predicting Customer Churn

Traditionally retai lers who recognize that churn is a costly problem that needs to be addressed rely on a team of data scientists to create analytical models that identify customers who are at risk of churn. This approach while being costly, as it requires a team of data scientists, analytical software tools etc, is also time consuming.

Building a churn solution typically takes several weeks to months. The data scientist needs to develop an analytical concept for churn, and then write the code necessary to build and run these models. Only then can the retai ler start identifying the churned customers and understand the changes in their shopping patterns, in order to be able to craft an effective and personalized win-back campaign strategy., This entire process can take 6 months to a year before the retai ler can execute his campaign strategy!

In today’s fast changing market place this approach has additional shortfal ls in its abil ity to adapt to the future. While the custom-built model may be applicable today, it will not be relevant a year from now.

According to a study by the Association of Consumer Research, customer attrition or churn in retail is as high as 20 percent. What this means is that in order to sustain your business at current levels or grow most retailers need to get better at retaining existing customers or be ready to spend considerable amount of money on acquiring new customers.

In competit ive markets where multiple retai lers are vying for the same customers, customer acquisit ion campaigns become very costly.

A better approach is to focus on effective customer retention campaigns. Numerous studies have said that customer retention has a stronger impact on bottom line that customer acquisit ion campaigns.

Cost of Churn

As a result, the model wil l need to be re-worked every other year to adapt to the changing climate, which creates more delays for the retai ler in addressing churn.

Because of these challenges, a number of retai lers have been turning to “out-of-the-box” churn solutions. In such solutions, the algorithms for churn are pre-packaged. The out-of-the-box solutions offer the quickest time to value, as the development process is almost non-existent. The churn solution can be implemented almost immediately, which allows the retai ler to immediately address churn issues, rather than having to spend months developing an applicable model.

How Manthan can helpManthan’s delivers a pre-packaged customer analytics solution that embeds sophisticated analytical algorithms under an intuit ive business interface that enable retai l markets recognize customers with waning loyalty and then act on that insight with personalized 1-1 engagement.

With Manthan’s Customer360, marketers can identify at risk of churn customers by analyzing historic customer behavior to recognize which customers have churned and the specific behavior they displayed before churning. It extrapolates these attributes to identify al l existing customers who are at risk of churn. Customer360 also scores the churn risk for each customer so marketers can priorit ize customers for retention campaigns.

Identifying at risk customers is a part of the solution, engaging these at risk customers with relevant messages in order win their loyalty back is the tougher challenge. Customer360 enables the marketer to execute retention campaigns that yield the desired business outcome.

First, Customer360 helps the marketer identify the most attractive segments for the retention campaign, based on parameters such as Li fe Time Value, Average Annual Spend, and Average Basket Size.

CUSTOMER 360

Let’s revisit the hypothetical retailer

mentioned earlier in the document, and

assume he is using Customer360 to

address his churn problem.

To start, the marketing manager

creates customer segments based on

shopping behavior. He then builds a

churn model by defining the churn as a

situation where customer has reduced

monthly spend by 70% & trend of

missing visits over the last 6 months. Of

the 2 million total customer base, the

company found that over 600,000

customers were at 80% risk for churn

which amounted to over $300 million in

lost revenue every year. Specifically, lost

sales due to churn among its most

valuable segment alone amounted to

over $120 million

The marketer identifies the behavioral

traits of the customer in their most

valuable segment and especially those

who are at risk of churn. Using these

insights the marketer designs a

retention campaign and delivers

personalized promotions to the at-risk

customers in this segment to re-engage

and win back their loyalty. The marketer

realizes a 22% success in retaining

customers who were likely to churn and

stops revenue loss of $6 million.

Next, Customer360 helps the marketer understand customer behavior and preferences at an individual level. The Marketer can use this insight to design and deliver personalized promotions that encourages positive responses from the most at-risk customers. Customer360 empowers the marketer to complete the journey f rom prob lem to so lu t ion – inc lud ing ins ights, dec is ions and action – inside its environment.

Identifying drivers of churn: There are various ways to evaluate the importance of an independent variable to be included in the model. One can go for a stepwise regression approach which would execute a forward (selecting one variable at a time) & backward (selecting all variables upfront) regression model to find the significance of the variables. The other technique that can be used is to study the weight of evidence (WOE) or importance value (IV) of each variable in distinguishing churner from non-churners. A correlation check among the independent variables also goes handy when it comes in variable selections.

Massaging the data for best output:Variable transformation is often required to transform to generate a robust model. Power transformation, log transformation, z-transformation are few of the techniques used to transform a variable.

Another key component of a modeling exercise is outl ier treatment & handling missing values. A simple graphical representation through box-plot or scatter plot would give a clear picture of the outl iers present in the data. Also, capping of a variable (below 1st percenti le & above 99th percenti le), sigma (a value lying beyond the tolerance l imits) & exponential smoothing of the outl iers are other approach useful in outl ier detection. Once an outl ier is detected, it can be either be removed from the analysis, or understand the pattern of it & take a business call to include or exclude it from the model. For missing value, a frequency chart of the variable would clearly reveal the amount of missing in it. It can be replaced by the mean or median of other values of the variable. Median is preferred as it is unaffected by outl iers. List-wise or pair-wise deletion of the values is also taken into consideration. Weighted average of the variable is another approach to tackle missing values in a data. Regression substitution is also often used to replace missing values.

Under the hood - Customer 360 churn modeling approach

Picking the right methodology: There are various methodologies to model churners. Decision tree, neural network, logistic regression, naïve bayes, survival techniques are few of them. Customer360 uses logistic regression as it is less data dependent, easy to interpret and robust in nature. It is useful as gives a probabil istic value to identify churners and a single equation model is easy to decipher. A typical logistic regression equation looks l ike:

Where, p is the estimated probabil ity, b0…….bp are the coefficient and x1………xp are the independent variables..

Teaming with Manthan

A dollar spent on retention is worth 3 dollars spent on acquisit ion. The key challenge in retention is the abil ity to identify the most profitable customers who are l ikely to defect and preempt attrit ion with relevant marketing actions.

Customer360 from Manthan, not only helps you identify the drivers of churn and predict which customers are l ikely to leave but also identify who amongst the at-risk customers are the most profitable and need to be retained to deliver business outcomes. The integrated personalization and execution capabil it ies in Customer360 enables marketers drive targeted retention campaigns with the right mix of offers and channels to mitigate the risks of churn.

Manthan serves as the Chief Analyt ics Officer for consumer industr ies wor ldwide. Manthan’s comprehensive port fo l io of analyt ics products and solut ions are architected with deep industry specifici ty,br inging together analyt ics, technology and industry pract ices to evolve sophist icated, yet intu i t ive analyt ical capabi l i ty. At Manthan, we understand the marketer and his issues.For more informat ion please v is i t : https://customer360.manthan.com